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Quality Assurance Procedures for CORIE Data Realtime QA Timeseries Diagram of Slopes Sequential Likelihood Ratio Archival QA Time Pressure Temperature.

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Presentation on theme: "Quality Assurance Procedures for CORIE Data Realtime QA Timeseries Diagram of Slopes Sequential Likelihood Ratio Archival QA Time Pressure Temperature."— Presentation transcript:

1 Quality Assurance Procedures for CORIE Data Realtime QA Timeseries Diagram of Slopes Sequential Likelihood Ratio Archival QA Time Pressure Temperature Salinity Velocity Meteorology Database Web Visualization Field Staff Near Real-time Quality Assessment Instrument Network Archival Quality Assessment Ascii Public Data Archive Field Notes Quality Flags

2 Real-time Quality Assurance Visual evaluation of data quality 4 times a week Automated testing for biofouling, checked by operator Real-time checks result in notification of field staff No database storage of notification No incorporation of assessment into database quality flags Non-automated use of assessment to deactivate web display of real-time data

3 Archival quality assurance CTD and ADP data quality assessment on a monthly basis. 1 month lag in assessment (January data tested at the end of February) CTD QA dependent on subsequent data ADP QA not dependent on subsequent data Data which passes QA is stored in ascii public archive QA not used to generate Quality flags in database FebMarApr CTD QA ADP QA

4 Timeseries Timeseries of Depth, Salinity, Temperature displayed on website Inspected for instrument failure or biofouling

5 Cross-estuary slope diagram S-T plot of all stations Almost all stations should produce the same s-t line Chnke, ogi01, and ogi02 are exceptions Mottb possibly biofouledExtensive biofouling

6 Sequential Likelihood Ratio Based on linear S-T relationship across estuary Accounts for local variation from linear relationship Depends on S and T at daily maximum S at each station, river T and Ocean T Modeled S clean and S biofouled based on T, T R, and T O compared to measured S Station specific ratio cutoff, trained on known biofouled data Used to generate a visual display Currently trained for lower estuary stations Extension of method to lateral bays under development Could be used for archival QA TRTR 34 TOTO 0 SMSM TMTM S cl S bf

7 CTD: time Radio network can produce data with bunched time values Expected timestep between data points is determined from data (median timestep) If timesteps are shorter than median time step, with a gap preceding bunch that has correct length, then data are reassigned times evenly spaced over gap If gap is longer than data clump, then data clump is discarded

8 CTD: pressure data Pressure data is corrected for atmospheric pressure using atmospheric pressure record from marsh or tansy Tested for spikes using a high pass filter (4 th order non-causal Butterworth filter with a cutoff period of 1 hour, implemented using the matlab function idfilt) spikes > 0.22 m removed Period (30 minutes) around spike is removed If tide period has extensive smaller spikes (mean noise > 0.01 m), entire period is removed

9 CTD: temperature Subject to range limits ( 30) Subject to visual inspection Instrument failure has generally produced extensive invalid values

10 CTD: Salinity Main concern is biofouling, but Conductivity sensors can also fail Sensor failure is detected by range check (S 35) and by visual inspection Biofouling is tested by using cross estuary s-t relationship Determine median s-t slope for each tidal period

11 CTD: Salinity Compare each instrument’s s-t slope for that tidal period to median Cutoff: abs(local slope) – abs(median slope) > 0.2 => biofouled When an instrument is considered biofouled, preceding data is considered biofouled until a clean cutoff is exceeded Clean cutoff: abs(local slope) – abs(median slope) < 0 When median slope approaches 0, method fails If instrument is biofouled after period of near-zero slope, then entire period of near-sero slope is considered biofouled

12 CTD: Salinity Automated assessment produces both false positives and false negatives Results are manually checked False positiveFalse negative Transient Biofouling

13 ADP: velocity 2 major sources of bad velocity data: Surface reflection Low signal strength –signal strength decreases with distance from instrument –decreases with biofouling of instrument over time 1 minor source: instrument roll-over Unlike Conductivity sensors, biofouling is easily determined because signal strength is a measured variable

14 ADP: velocity Signal strength < 20 dB, data flagged bad Documentation recommends a cutoff of 9 dB, but testing indicates that 9 dB cutoff admits some questionable data Surface reflection detected by increasing signal strength Signal strength increase can also be caused by variation in reflective material in water Determine approximate surface from pressure record, check for signal strength increase within 3 m of approximate surface Surface Reflection Signal Strength

15 Meteorological Data Subject to physical range tests and visual inspection Further methods under development

16 Storage of Quality Assessment Data records which do not meet minimal quality standards are stored in the raw data files, but do not enter the database Notices of observer suspicion of data quality are not currently stored in a formal manner, and are not entered into the database Archival quality assurance procedures currently generate public archive files which contain only data which has passed the quality assurance testspublic archive files The quality assurance flagging is not currently stored in the database

17 End

18 Models A model of the clean signal –Temperature and salinity variation are correlated. Model daily maximum salinity and corresponding temperature are jointly Gaussian. –The probability density for observing the sequence of salinity measurements {s n }, given the sequence of recorded mixing coefficients {T n }  and a clean sensor p({s n } | {  n }  clean ) A model of the biofouled signal –Allows for different degradation rates m for each biofouling episode, and arbitrary onset time  with these parameters fit to incoming data. p({s n } | {  n }  m  biofouled ) = p ({s n } | {  n }  biofouled ) –m and  are unknown –These parameters are fit to the data sequence by maximum likelihood.

19 Regression Model: Mixture of Experts The correlation between salinity and temperatures is not stationary. –The detector system needs to switch between seasons. –A mixture of local models can cover different behaviors. Both of experts and gating network receive same input vector. Each expert network tackles each of the different seasons. The gating network decides which of the experts should be used. Regression output Expert Network 1 Expert Network 2 Expert Network n  Gating Network Input vector T Output nn   g1g1 gngn g2g2  Ref.

20 Approach and Results Parameterized novelty detectors embedded in a sequential likelihood ratio test –SLR at current time N is compared to a threshold to identify biofouling events. Results –Automated biofouling detectors deployed throughout the estuary. Monitored by observer, and used to send out notices of biofouling events, but not incorporated directly in to data flagging.detectors Ref.

21 Criteria for rejecting data before it enters the database rserial2db rejects data lines based on failed checksum or garbled line Short input line: [RE^M], skipping. Skipping unknown data line: [abedCT D , , *6F] Checksum failed for data line: W,üR'¢í?»TW%X¯»U»PT$CRdsdmaRV0CTDd00730R seabedCT D , , *60 Short input line: [], skipping. Skipping unknown data line: [W,ýS'¢è¾?»T W%Y­»S»UT10394A :0 746:1 :2 :3 :4 532: :6 :7] Line length = 162, must be 81 to 83 chars long, skipping data line: 10395A :0 770:1 :2 :3 :4 282:5 : A :0 770:1 :2 :3 :4 278:5 :6 :7 Most data is not subjected to sanity check (e.g salinity 35) Certain stations are handled as special cases and are subject to sanity checks (ogi02 is checked for negative sal, temp, and cond)

22 Depth spikes removed

23 Slope Comparison

24 Salinity Flagging


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